da Costa Milene Rangel, Dos Santos Júnior Bráulio, da Silva Santos Marisa
Centre of Health Technology Assessment, National Institute of Cardiology, Rio de Janeiro, Brazil.
Faculty of Pharmacy, Federal University of Rio de Janeiro, Rio de Janeiro, Brazil.
Pharmacoecon Open. 2025 May 31. doi: 10.1007/s41669-025-00586-y.
Decision analytical models are typically included in health economic evaluations to represent clinical pathways and enable the estimation of clinical and economic outcomes of health technologies. Clinical effects are frequently measured in terms of health-related quality of life and expressed as utility values. It is not rare that a health state in an analytical model simultaneously comprises more than one health condition. In this situation, the utility of each coexisting health condition could be combined using the additive, multiplicative, minimum, or adjusted decrement estimator (ADE) methods. However, there is no consensus about the best approach. This study aimed to compare different methods to estimate utility values for health states in which patients carry more than one health condition using data from the Brazilian population.
Data were obtained from a multicentric cross-sectional evaluation study conducted in Brazil. Individuals completed the EQ-5D-3L questionnaire, a generic preference-based instrument that is used to obtain utility values, and were requested to disclose if they had any health conditions. Utilities were obtained according to the Brazilian value set. Four methods for adjusting joint utilities were tested: additive, multiplicative, minimum, and ADE. Observed and estimated utility values were compared for accuracy and bias.
A total of 5774 individuals were included in the analysis. The utility score (mean ± SE) was 0.8235 ± 0.1717. Lower utility scores were associated with an increased number of comorbidities, reaching 0.467 ± 0.192 for individuals with seven conditions. The minimum method produced accurate utility estimates for individuals with two simultaneous health conditions. For health states with more than two conditions, the multiplicative method presented more accurate estimates. Overall, fixing the baseline utility equal to the mean utility of healthy individuals produced less biased estimates compared with a baseline utility equal to 1.
Depending on the utility data available and the number of concomitant conditions, different adjustment methods could be used that produce accurate estimates. For the adjustment of Brazilian utility values for health states with comorbidities, the minimum and multiplicative methods should be preferred if two or more than two conditions are present, respectively.
决策分析模型通常包含在卫生经济评估中，以呈现临床路径并估算卫生技术的临床和经济结果。临床效果常依据与健康相关的生活质量来衡量，并以效用值表示。在分析模型中，一种健康状态同时包含不止一种健康状况的情况并不罕见。在这种情形下，每种共存健康状况的效用可使用相加、相乘、最小值或调整减量估计器（ADE）方法进行合并。然而，对于最佳方法尚无共识。本研究旨在利用巴西人群的数据，比较估算患有不止一种健康状况的患者健康状态效用值的不同方法。
数据取自巴西开展的一项多中心横断面评估研究。个体完成EQ-5D-3L问卷，这是一种基于偏好的通用工具，用于获取效用值，并被要求披露其是否患有任何健康状况。根据巴西价值集获取效用值。测试了四种调整联合效用的方法：相加、相乘、最小值和ADE。比较观察到的和估计的效用值的准确性和偏差。
共有5774名个体纳入分析。效用得分（均值±标准误）为0.8235±0.1717。较低的效用得分与共病数量增加相关，患有七种疾病的个体效用得分达到0.467±0.192。最小值方法对同时患有两种健康状况的个体产生了准确的效用估计。对于患有两种以上疾病的健康状态，相乘方法给出了更准确的估计。总体而言，与将基线效用设定为1相比，将基线效用设定为健康个体的平均效用产生的偏差估计更少。
根据可用的效用数据和伴随状况的数量，可使用不同的调整方法以产生准确的估计。对于合并症健康状态的巴西效用值调整，如果存在两种或两种以上状况，分别应优先选择最小值和相乘方法。
